Relationship between coking properties measured by automatic Sapozhnikov plastometer with other measurements
Bibliographic record
Abstract
A Sapozhnikov plastometer is equipment that is commonly used, particularly in Asia, for measuring the contraction of the coal bed as well as the plastic layer thickness during the coking process. The relationship between the Sapozhnikov contraction X and the maximum plastic layer thickness Y values and other thermal rheology testing techniques is not fully understood. It leads to difficulties in the interpretation of data using different thermal rheology properties measurement techniques. In this work, a series of coal samples were analyzed in parallel using the automatic Sapozhnikov plastometer, the Gieseler plastometer, and the Ruhr dilatometer. The measurement results were compared to assist in the interpretation of data generated using different coal thermal rheology property measurement techniques. The Sapozhnikov X results are found not comparable due to the difference in sample particle size and testing conditions for component coals with the wide range of maximum mean reflectance (Ro), but a significant linear correlation on the contraction/expansion behavior between Sapozhnikov and Ruhr dilatation test is observed for experimental coal blends Ro between 1.00 and 1.20. Sapozhnikov Y results are linearly related to the melting range from both the Gieseler plastometer and the Ruhr dilatometer for all the experimental single coals and coal blends.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".